Overview

Dataset statistics

Number of variables21
Number of observations1299
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory682.3 KiB
Average record size in memory537.9 B

Variable types

NUM15
CAT5
UNSUPPORTED1

Reproduction

Analysis started2021-02-21 21:32:44.068047
Analysis finished2021-02-21 21:33:18.420617
Duration34.35 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
title has a high cardinality: 1291 distinct values High cardinality
deltaMedianPrice is highly correlated with price and 1 other fieldsHigh correlation
price is highly correlated with deltaMedianPriceHigh correlation
deltaAvgPrice is highly correlated with deltaMedianPriceHigh correlation
dublinNorthSouth is highly correlated with neighbourhoodHigh correlation
neighbourhood is highly correlated with dublinNorthSouthHigh correlation
title is uniformly distributed Uniform
df_index has unique values Unique
floorArea is an unsupported type, check if it needs cleaning or further analysis Unsupported
deltaMedianPrice has 53 (4.1%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

UNIQUE
Distinct count1299
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1512.7397998460353
Minimum6
Maximum3030
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:18.514727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile222.5
Q1841.5
median1507
Q32175
95-th percentile2821.6
Maximum3030
Range3024
Interquartile range (IQR)1333.5

Descriptive statistics

Standard deviation816.3766833
Coefficient of variation (CV)0.5396676173
Kurtosis-1.06988594
Mean1512.7398
Median Absolute Deviation (MAD)667
Skewness0.0181515652
Sum1965049
Variance666470.8891
2021-02-21T21:33:18.643876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2048 1 0.1%
 
1279 1 0.1%
 
2834 1 0.1%
 
1311 1 0.1%
 
1310 1 0.1%
 
1307 1 0.1%
 
1306 1 0.1%
 
1305 1 0.1%
 
1303 1 0.1%
 
1302 1 0.1%
 
Other values (1289) 1289 99.2%
 
ValueCountFrequency (%) 
6 1 0.1%
 
11 1 0.1%
 
12 1 0.1%
 
13 1 0.1%
 
15 1 0.1%
 
ValueCountFrequency (%) 
3030 1 0.1%
 
3028 1 0.1%
 
3026 1 0.1%
 
3009 1 0.1%
 
3005 1 0.1%
 

title
Categorical

HIGH CARDINALITY
UNIFORM
Distinct count1291
Unique (%)99.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Apartment 101, Milltown Hall, Milltown Avenue, Mount Saint Annes, Milltown, Dublin 6
 
2
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24
 
2
88 Scholarstown Park, Rathfarnham, Dublin 16
 
2
26 Grace Park Court, Beaumont Road, Beaumont, Dublin 9
 
2
20 Muckross Green, Perrystown, Dublin 12
 
2
Other values (1286)
1289
ValueCountFrequency (%) 
Apartment 101, Milltown Hall, Milltown Avenue, Mount Saint Annes, Milltown, Dublin 6 2 0.2%
 
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24 2 0.2%
 
88 Scholarstown Park, Rathfarnham, Dublin 16 2 0.2%
 
26 Grace Park Court, Beaumont Road, Beaumont, Dublin 9 2 0.2%
 
20 Muckross Green, Perrystown, Dublin 12 2 0.2%
 
20 Moyclare Road, Baldoyle, Dublin 13 2 0.2%
 
Apartment 427, The Old Chocolate Factory, Kilmainham Square, Kilmainham, Dublin 8 2 0.2%
 
171 Drimnagh Road, Drimnagh, Dublin 12 2 0.2%
 
36 Cloyne Road, Kimmage, Dublin 12 1 0.1%
 
Apartment 39, Rope Walk Place, Ringsend, Dublin 4 1 0.1%
 
Other values (1281) 1281 98.6%
 
2021-02-21T21:33:18.775668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length84
Mean length45.08929946
Min length22
ValueCountFrequency (%) 
Lowercase_Letter 27 38.0%
 
Uppercase_Letter 25 35.2%
 
Decimal_Number 10 14.1%
 
Other_Punctuation 4 5.6%
 
Connector_Punctuation 1 1.4%
 
Close_Punctuation 1 1.4%
 
Dash_Punctuation 1 1.4%
 
Open_Punctuation 1 1.4%
 
Space_Separator 1 1.4%
 
ValueCountFrequency (%) 
Latin 52 73.2%
 
Common 19 26.8%
 
ValueCountFrequency (%) 
ASCII 70 100.0%
 

neighbourhood
Categorical

HIGH CORRELATION
Distinct count22
Unique (%)1.7%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Dublin 15
 
124
Dublin 11
 
107
Dublin 8
 
91
Dublin 9
 
85
Dublin 3
 
84
Other values (17)
808
ValueCountFrequency (%) 
Dublin 15 124 9.5%
 
Dublin 11 107 8.2%
 
Dublin 8 91 7.0%
 
Dublin 9 85 6.5%
 
Dublin 3 84 6.5%
 
Dublin 4 75 5.8%
 
Dublin 14 74 5.7%
 
Dublin 24 71 5.5%
 
Dublin 12 71 5.5%
 
Dublin 7 69 5.3%
 
Other values (12) 448 34.5%
 
2021-02-21T21:33:18.904655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length9
Mean length8.541185527
Min length8
ValueCountFrequency (%) 
Decimal_Number 10 55.6%
 
Lowercase_Letter 5 27.8%
 
Uppercase_Letter 2 11.1%
 
Space_Separator 1 5.6%
 
ValueCountFrequency (%) 
Common 11 61.1%
 
Latin 7 38.9%
 
ValueCountFrequency (%) 
ASCII 18 100.0%
 

propertyType
Categorical

Distinct count10
Unique (%)0.8%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Apartment
403
Terrace
342
Semi-D
292
End of Terrace
117
Detached
 
67
Other values (5)
 
78
ValueCountFrequency (%) 
Apartment 403 31.0%
 
Terrace 342 26.3%
 
Semi-D 292 22.5%
 
End of Terrace 117 9.0%
 
Detached 67 5.2%
 
Duplex 31 2.4%
 
Bungalow 18 1.4%
 
Site 14 1.1%
 
Townhouse 14 1.1%
 
Studio 1 0.1%
 
2021-02-21T21:33:19.032315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length14
Mean length8.056197075
Min length4
ValueCountFrequency (%) 
Lowercase_Letter 19 70.4%
 
Uppercase_Letter 6 22.2%
 
Dash_Punctuation 1 3.7%
 
Space_Separator 1 3.7%
 
ValueCountFrequency (%) 
Latin 25 92.6%
 
Common 2 7.4%
 
ValueCountFrequency (%) 
ASCII 27 100.0%
 

numBedrooms
Real number (ℝ)

Distinct count9
Unique (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6243264049268666
Minimum-1.0
Maximum11.0
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:19.147824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum11
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.028361782
Coefficient of variation (CV)0.391857423
Kurtosis4.868244502
Mean2.624326405
Median Absolute Deviation (MAD)1
Skewness0.4171197651
Sum3409
Variance1.057527955
2021-02-21T21:33:19.249783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3 525 40.4%
 
2 472 36.3%
 
4 136 10.5%
 
1 102 7.9%
 
5 42 3.2%
 
-1 15 1.2%
 
6 4 0.3%
 
7 2 0.2%
 
11 1 0.1%
 
ValueCountFrequency (%) 
-1 15 1.2%
 
1 102 7.9%
 
2 472 36.3%
 
3 525 40.4%
 
4 136 10.5%
 
ValueCountFrequency (%) 
11 1 0.1%
 
7 2 0.2%
 
6 4 0.3%
 
5 42 3.2%
 
4 136 10.5%
 

numBathrooms
Real number (ℝ)

Distinct count7
Unique (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6274056966897614
Minimum-1.0
Maximum6.0
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:19.369238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8362291347
Coefficient of variation (CV)0.5138418382
Kurtosis2.413610985
Mean1.627405697
Median Absolute Deviation (MAD)1
Skewness0.677925622
Sum2114
Variance0.6992791658
2021-02-21T21:33:19.506465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 633 48.7%
 
2 479 36.9%
 
3 144 11.1%
 
-1 18 1.4%
 
4 18 1.4%
 
5 5 0.4%
 
6 2 0.2%
 
ValueCountFrequency (%) 
-1 18 1.4%
 
1 633 48.7%
 
2 479 36.9%
 
3 144 11.1%
 
4 18 1.4%
 
ValueCountFrequency (%) 
6 2 0.2%
 
5 5 0.4%
 
4 18 1.4%
 
3 144 11.1%
 
2 479 36.9%
 

floorArea
Unsupported

REJECTED
UNSUPPORTED
Missing0
Missing (%)0.0%
Memory size10.3 KiB

price
Real number (ℝ≥0)

HIGH CORRELATION
Distinct count187
Unique (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393785.2193995381
Minimum75000.0
Maximum2500000.0
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:19.611174image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile195000
Q1260000
median345000
Q3450000
95-th percentile795000
Maximum2500000
Range2425000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation220680.4631
Coefficient of variation (CV)0.5604081926
Kurtosis21.37023469
Mean393785.2194
Median Absolute Deviation (MAD)95000
Skewness3.441321648
Sum511527000
Variance4.869986678e+10
2021-02-21T21:33:19.700716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
275000 42 3.2%
 
350000 41 3.2%
 
250000 37 2.8%
 
395000 35 2.7%
 
375000 33 2.5%
 
425000 33 2.5%
 
325000 31 2.4%
 
295000 31 2.4%
 
450000 30 2.3%
 
260000 29 2.2%
 
Other values (177) 957 73.7%
 
ValueCountFrequency (%) 
75000 1 0.1%
 
95000 1 0.1%
 
135000 2 0.2%
 
139000 1 0.1%
 
140000 3 0.2%
 
ValueCountFrequency (%) 
2500000 2 0.2%
 
2300000 1 0.1%
 
1800000 1 0.1%
 
1700000 1 0.1%
 
1600000 2 0.2%
 

rating
Categorical

Distinct count16
Unique (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
D2
173
D1
155
C2
129
C3
124
C1
 
112
Other values (11)
606
ValueCountFrequency (%) 
D2 173 13.3%
 
D1 155 11.9%
 
C2 129 9.9%
 
C3 124 9.5%
 
C1 112 8.6%
 
E1 105 8.1%
 
B3 92 7.1%
 
E2 88 6.8%
 
F 81 6.2%
 
G 70 5.4%
 
Other values (6) 170 13.1%
 
2021-02-21T21:33:19.811563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length6
Mean length2.076212471
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 10 66.7%
 
Decimal_Number 4 26.7%
 
Connector_Punctuation 1 6.7%
 
ValueCountFrequency (%) 
Latin 10 66.7%
 
Common 5 33.3%
 
ValueCountFrequency (%) 
ASCII 15 100.0%
 

sellerId
Real number (ℝ≥0)

Distinct count195
Unique (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5756.895304080062
Minimum7
Maximum11902
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:19.908825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q11367
median6962
Q39737
95-th percentile11062
Maximum11902
Range11895
Interquartile range (IQR)8370

Descriptive statistics

Standard deviation4203.177831
Coefficient of variation (CV)0.7301119109
Kurtosis-1.645741789
Mean5756.895304
Median Absolute Deviation (MAD)3987
Skewness-0.09081358387
Sum7478207
Variance17666703.88
2021-02-21T21:33:19.999343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11062 45 3.5%
 
10947 44 3.4%
 
11 39 3.0%
 
7569 39 3.0%
 
8505 35 2.7%
 
1413 33 2.5%
 
12 31 2.4%
 
3658 29 2.2%
 
6498 27 2.1%
 
9172 24 1.8%
 
Other values (185) 953 73.4%
 
ValueCountFrequency (%) 
7 6 0.5%
 
11 39 3.0%
 
12 31 2.4%
 
49 13 1.0%
 
56 8 0.6%
 
ValueCountFrequency (%) 
11902 5 0.4%
 
11899 1 0.1%
 
11766 5 0.4%
 
11754 1 0.1%
 
11720 2 0.2%
 

longitude
Real number (ℝ)

Distinct count1265
Unique (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.281105550088232
Minimum-6.443882
Maximum-6.055211
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:20.107653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.443882
5-th percentile-6.403495
Q1-6.32122465
median-6.276468
Q3-6.235783
95-th percentile-6.1698893
Maximum-6.055211
Range0.388671
Interquartile range (IQR)0.08544165

Descriptive statistics

Standard deviation0.07043407581
Coefficient of variation (CV)-0.01121364308
Kurtosis-0.04100664671
Mean-6.28110555
Median Absolute Deviation (MAD)0.041914
Skewness-0.08297661482
Sum-8159.15611
Variance0.004960959035
2021-02-21T21:33:20.226765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-6.227282 3 0.2%
 
-6.309374 3 0.2%
 
-6.264954 2 0.2%
 
-6.23838 2 0.2%
 
-6.2834 2 0.2%
 
-6.264883 2 0.2%
 
-6.31234 2 0.2%
 
-6.248074 2 0.2%
 
-6.172943 2 0.2%
 
-6.27907 2 0.2%
 
Other values (1255) 1277 98.3%
 
ValueCountFrequency (%) 
-6.443882 1 0.1%
 
-6.443585 1 0.1%
 
-6.442693 1 0.1%
 
-6.441123 1 0.1%
 
-6.440754 1 0.1%
 
ValueCountFrequency (%) 
-6.055211 1 0.1%
 
-6.059735 1 0.1%
 
-6.065742 1 0.1%
 
-6.066756 1 0.1%
 
-6.06714 1 0.1%
 

latitude
Real number (ℝ≥0)

Distinct count1264
Unique (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.3444065219388
Minimum53.21904
Maximum53.433172
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:20.381034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum53.21904
5-th percentile53.2713847
Q153.318724
median53.34549
Q353.3811215
95-th percentile53.40107415
Maximum53.433172
Range0.214132
Interquartile range (IQR)0.0623975

Descriptive statistics

Standard deviation0.0415737757
Coefficient of variation (CV)0.0007793464846
Kurtosis-0.6420920591
Mean53.34440652
Median Absolute Deviation (MAD)0.031599
Skewness-0.4105714573
Sum69294.38407
Variance0.001728378826
2021-02-21T21:33:20.465985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
53.342931 3 0.2%
 
53.341431 3 0.2%
 
53.34549 2 0.2%
 
53.347308 2 0.2%
 
53.31296 2 0.2%
 
53.314145 2 0.2%
 
53.379986 2 0.2%
 
53.26935 2 0.2%
 
53.399981 2 0.2%
 
53.313725 2 0.2%
 
Other values (1254) 1277 98.3%
 
ValueCountFrequency (%) 
53.21904 1 0.1%
 
53.226358 1 0.1%
 
53.228438 1 0.1%
 
53.229695 1 0.1%
 
53.233209 1 0.1%
 
ValueCountFrequency (%) 
53.433172 1 0.1%
 
53.432174 1 0.1%
 
53.431643 1 0.1%
 
53.422947 1 0.1%
 
53.422198 1 0.1%
 

pricePerBedroom
Real number (ℝ)

Distinct count297
Unique (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146597.81516917775
Minimum-1500000.0
Maximum625000.0
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:20.552837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1500000
5-th percentile76666.66667
Q1107500
median137500
Q3184250
95-th percentile274550
Maximum625000
Range2125000
Interquartile range (IQR)76750

Descriptive statistics

Standard deviation99775.92229
Coefficient of variation (CV)0.6806098861
Kurtosis80.02294961
Mean146597.8152
Median Absolute Deviation (MAD)37500
Skewness-5.54567141
Sum190430561.9
Variance9955234668
2021-02-21T21:33:20.636557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
125000 34 2.6%
 
150000 34 2.6%
 
175000 29 2.2%
 
137500 23 1.8%
 
95000 21 1.6%
 
112500 20 1.5%
 
187500 20 1.5%
 
165000 19 1.5%
 
131666.6667 18 1.4%
 
115000 18 1.4%
 
Other values (287) 1063 81.8%
 
ValueCountFrequency (%) 
-1500000 1 0.1%
 
-850000 2 0.2%
 
-745000 1 0.1%
 
-498000 1 0.1%
 
-350000 1 0.1%
 
ValueCountFrequency (%) 
625000 2 0.2%
 
600000 1 0.1%
 
497500 1 0.1%
 
460000 1 0.1%
 
450000 2 0.2%
 

deltaAvgPrice
Real number (ℝ)

HIGH CORRELATION
Distinct count799
Unique (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18200.619426402525
Minimum-1781852.1739130435
Maximum488147.82608695654
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:20.721728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1781852.174
5-th percentile-276450.8424
Q1-39536.91275
median42167.40088
Q3106614.7018
95-th percentile270454.5911
Maximum488147.8261
Range2270000
Interquartile range (IQR)146151.6145

Descriptive statistics

Standard deviation192959.968
Coefficient of variation (CV)10.60183522
Kurtosis19.73630525
Mean18200.61943
Median Absolute Deviation (MAD)72731.82393
Skewness-2.904687946
Sum23642604.63
Variance3.723354925e+10
2021-02-21T21:33:20.801305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-20409.83607 8 0.6%
 
65231.25 7 0.5%
 
55231.25 7 0.5%
 
16102.94118 7 0.5%
 
93459.45946 7 0.5%
 
62167.40088 7 0.5%
 
14529.80132 6 0.5%
 
343147.8261 6 0.5%
 
102167.4009 6 0.5%
 
68459.45946 6 0.5%
 
Other values (789) 1232 94.8%
 
ValueCountFrequency (%) 
-1781852.174 2 0.2%
 
-1581852.174 1 0.1%
 
-1334772.358 1 0.1%
 
-1054396.947 1 0.1%
 
-1007273.973 1 0.1%
 
ValueCountFrequency (%) 
488147.8261 1 0.1%
 
468147.8261 1 0.1%
 
446603.0534 1 0.1%
 
435603.0534 1 0.1%
 
428147.8261 1 0.1%
 

deltaMedianPrice
Real number (ℝ)

HIGH CORRELATION
ZEROS
Distinct count275
Unique (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-30248.267898383372
Minimum-1990000.0
Maximum326000.0
Zeros53
Zeros (%)4.1%
Memory size10.3 KiB
2021-02-21T21:33:20.892109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1990000
5-th percentile-345400
Q1-73500
median5000
Q365000
95-th percentile170000
Maximum326000
Range2316000
Interquartile range (IQR)138500

Descriptive statistics

Standard deviation193812.0509
Coefficient of variation (CV)-6.407376831
Kurtosis26.72262439
Mean-30248.2679
Median Absolute Deviation (MAD)65000
Skewness-3.785778077
Sum-39292500
Variance3.756311108e+10
2021-02-21T21:33:20.985358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 53 4.1%
 
50000 32 2.5%
 
25000 30 2.3%
 
75000 29 2.2%
 
100000 29 2.2%
 
20000 28 2.2%
 
15000 23 1.8%
 
-10000 22 1.7%
 
55000 21 1.6%
 
65000 20 1.5%
 
Other values (265) 1012 77.9%
 
ValueCountFrequency (%) 
-1990000 2 0.2%
 
-1790000 1 0.1%
 
-1405000 1 0.1%
 
-1175000 1 0.1%
 
-1090000 2 0.2%
 
ValueCountFrequency (%) 
326000 1 0.1%
 
315000 1 0.1%
 
305000 1 0.1%
 
290000 2 0.2%
 
285000 2 0.2%
 

dublinNorthSouth
Categorical

HIGH CORRELATION
Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
S
673
N
626
ValueCountFrequency (%) 
S 673 51.8%
 
N 626 48.2%
 
2021-02-21T21:33:21.102982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length1
Mean length1
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 2 100.0%
 
ValueCountFrequency (%) 
Latin 2 100.0%
 
ValueCountFrequency (%) 
ASCII 2 100.0%
 

distToCity
Real number (ℝ≥0)

Distinct count1268
Unique (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.870918474761994
Minimum0.09991156306907832
Maximum17.012878032749462
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:21.198568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09991156307
5-th percentile1.341016621
Q13.060153685
median5.08424187
Q38.606567924
95-th percentile12.02436493
Maximum17.01287803
Range16.91296647
Interquartile range (IQR)5.546414238

Descriptive statistics

Standard deviation3.458445228
Coefficient of variation (CV)0.589080779
Kurtosis-0.5518558965
Mean5.870918475
Median Absolute Deviation (MAD)2.565375638
Skewness0.5426284354
Sum7626.323099
Variance11.9608434
2021-02-21T21:33:21.289809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.415355904 3 0.2%
 
3.590435795 3 0.2%
 
2.213178012 2 0.2%
 
8.438129316 2 0.2%
 
1.841780665 2 0.2%
 
4.401617511 2 0.2%
 
1.986795776 2 0.2%
 
5.764686977 2 0.2%
 
0.937056004 2 0.2%
 
0.4831172059 2 0.2%
 
Other values (1258) 1277 98.3%
 
ValueCountFrequency (%) 
0.09991156307 1 0.1%
 
0.1835478493 1 0.1%
 
0.2065863985 2 0.2%
 
0.3172734398 1 0.1%
 
0.3828696698 1 0.1%
 
ValueCountFrequency (%) 
17.01287803 1 0.1%
 
16.56325022 1 0.1%
 
16.47248576 1 0.1%
 
16.37153247 1 0.1%
 
16.35529162 1 0.1%
 

daysSincePublished
Real number (ℝ≥0)

Distinct count155
Unique (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.3448806774442
Minimum2
Maximum333
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:21.389613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q147
median81
Q3107
95-th percentile144
Maximum333
Range331
Interquartile range (IQR)60

Descriptive statistics

Standard deviation42.41735818
Coefficient of variation (CV)0.5345947694
Kurtosis2.044387728
Mean79.34488068
Median Absolute Deviation (MAD)28
Skewness0.585147297
Sum103069
Variance1799.232275
2021-02-21T21:33:21.497769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
97 36 2.8%
 
68 35 2.7%
 
48 35 2.7%
 
80 27 2.1%
 
109 25 1.9%
 
100 25 1.9%
 
81 25 1.9%
 
88 24 1.8%
 
32 24 1.8%
 
108 23 1.8%
 
Other values (145) 1020 78.5%
 
ValueCountFrequency (%) 
2 6 0.5%
 
3 9 0.7%
 
4 3 0.2%
 
5 8 0.6%
 
6 9 0.7%
 
ValueCountFrequency (%) 
333 1 0.1%
 
330 1 0.1%
 
297 1 0.1%
 
223 1 0.1%
 
222 1 0.1%
 

numFood
Real number (ℝ≥0)

Distinct count44
Unique (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.47036181678214
Minimum11
Maximum59
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:21.601977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile30
Q142
median49
Q352
95-th percentile56
Maximum59
Range48
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.097803966
Coefficient of variation (CV)0.1742573901
Kurtosis1.566891463
Mean46.47036182
Median Absolute Deviation (MAD)4
Skewness-1.26027652
Sum60365
Variance65.57442907
2021-02-21T21:33:21.683437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
51 113 8.7%
 
52 103 7.9%
 
49 88 6.8%
 
53 87 6.7%
 
48 85 6.5%
 
50 78 6.0%
 
54 58 4.5%
 
55 58 4.5%
 
47 54 4.2%
 
46 52 4.0%
 
Other values (34) 523 40.3%
 
ValueCountFrequency (%) 
11 2 0.2%
 
13 1 0.1%
 
15 2 0.2%
 
16 2 0.2%
 
19 2 0.2%
 
ValueCountFrequency (%) 
59 2 0.2%
 
58 9 0.7%
 
57 26 2.0%
 
56 48 3.7%
 
55 58 4.5%
 

numRecreation
Real number (ℝ≥0)

Distinct count23
Unique (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.291762894534257
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:21.768114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median13
Q315
95-th percentile18
Maximum23
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.847879958
Coefficient of variation (CV)0.3130454103
Kurtosis-0.1041382073
Mean12.29176289
Median Absolute Deviation (MAD)2
Skewness-0.4682915977
Sum15967
Variance14.80618017
2021-02-21T21:33:21.850267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12 144 11.1%
 
15 144 11.1%
 
13 143 11.0%
 
14 131 10.1%
 
11 116 8.9%
 
16 99 7.6%
 
10 80 6.2%
 
17 75 5.8%
 
9 70 5.4%
 
18 51 3.9%
 
Other values (13) 246 18.9%
 
ValueCountFrequency (%) 
1 4 0.3%
 
2 4 0.3%
 
3 16 1.2%
 
4 24 1.8%
 
5 44 3.4%
 
ValueCountFrequency (%) 
23 1 0.1%
 
22 3 0.2%
 
21 3 0.2%
 
20 6 0.5%
 
19 22 1.7%
 

numShop
Real number (ℝ≥0)

Distinct count36
Unique (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.85604311008468
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Memory size10.3 KiB
2021-02-21T21:33:21.936029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median7
Q317
95-th percentile30
Maximum36
Range35
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.642803862
Coefficient of variation (CV)0.796128366
Kurtosis0.003035512463
Mean10.85604311
Median Absolute Deviation (MAD)3
Skewness1.070896143
Sum14102
Variance74.6980586
2021-02-21T21:33:22.022822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4 348 26.8%
 
5 118 9.1%
 
7 79 6.1%
 
6 76 5.9%
 
2 45 3.5%
 
9 39 3.0%
 
11 37 2.8%
 
8 37 2.8%
 
20 35 2.7%
 
3 32 2.5%
 
Other values (26) 453 34.9%
 
ValueCountFrequency (%) 
1 11 0.8%
 
2 45 3.5%
 
3 32 2.5%
 
4 348 26.8%
 
5 118 9.1%
 
ValueCountFrequency (%) 
36 1 0.1%
 
35 2 0.2%
 
34 7 0.5%
 
33 26 2.0%
 
32 10 0.8%
 

Interactions

2021-02-21T21:32:48.559784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:48.703150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:48.826143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:48.954811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.072838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.193595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.329877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.437738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.550615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.661108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.773139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:49.889319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.003762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.116347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.229286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.346499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.459387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.566314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.680676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.792952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:50.906539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.025177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.129668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.240623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.352557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.461701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.572609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.680414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.785507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.886717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:51.996531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:52.110402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:52.580789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:52.708655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:52.824431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:52.940982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.063704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.172037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.293344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.405947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.515958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:32:53.753233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.864578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:53.973701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.093121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.202409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.311329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.425942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.534659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.644506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.762085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.862586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:54.968693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.072902image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.179782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.295111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.406518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.513886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.615640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.725525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.836957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:55.943958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.059875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.169447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.281764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.400225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.504338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.612727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.720506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.828335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:56.942054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.054508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.171551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.276616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.387829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.507892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.624495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.750125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.869896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:57.994186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.130692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.244088image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.364390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.481749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.600376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.725830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.848292image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:58.966730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.084894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.208021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.307867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.406871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.509127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.609261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.710593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.819825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:32:59.914064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:00.015478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:00.119247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:00.217252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:00.322042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:00.424627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:00.918046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.027070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.131345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.237931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.341901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.450358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.555392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.663373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:01.777815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:01.980442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.084593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.187530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.296055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.402528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.505113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.605956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.716253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.823627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:02.926457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:03.035597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:05.764809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:05.877849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:05.988880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.119150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.235994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.374019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.502624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.625282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.768643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.882830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:06.996054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:07.120635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:07.234246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:07.747577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:07.925332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:08.070432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:08.217018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:08.364756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:08.486669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:08.611744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:09.148823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:10.404904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:16.550294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-02-21T21:33:16.899845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:17.066758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:17.227780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-21T21:33:22.147759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-21T21:33:22.376606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-21T21:33:22.592642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-21T21:33:22.818691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-02-21T21:33:23.030778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-02-21T21:33:17.611220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-21T21:33:18.199912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
06Apartment 172, Block C, Dublin 7Dublin 7Apartment1.01.051250000.0C11331-6.27758453.348715250000.000000137061.403509100000.0N1.387431353134
111Apartment 48, Beacon, Ashtown, Dublin 15Dublin 15Apartment1.01.045210000.0C14274-6.31055953.376695210000.000000127167.40088175000.0N4.3613611248183
212Apartment 15, Blackhall Court, Stoneybatter, Dublin 7Dublin 7Apartment2.01.046250000.0E2948-6.28213453.349442125000.000000137061.403509100000.0N1.651646951134
313Apartment 127, Block B, Lymewood Mews, Northwood, Santry, Dublin 9Dublin 9Apartment2.02.070275000.0C23658-6.25641953.402848137500.000000114529.801325100000.0N5.529245549119
41546 Oxmantown Road, Stoneybatter, Dublin 7Dublin 7Terrace2.0-1.056280000.0F1087-6.29165453.353738140000.000000107061.40350970000.0N2.233330952124
51871 Parklands Court, Ballycullen, Dublin 16Dublin 16Apartment2.01.070255000.0ZZZ8210-6.34142853.274936127500.000000237726.027397220000.0S10.301642928923
61924 The Wood, Millbrook Lawns, Tallaght, Dublin 24Dublin 24Terrace3.01.090285000.0ZZZ8210-6.35503353.28175995000.0000004899.2248060.0S10.214784937731
74243 Woodlawn Drive, Santry, Dublin 9Dublin 9Semi-D3.03.097350000.0D29405-6.23325753.401033116666.66666739529.80132525000.0N5.5733873521213
848Apartment 201, The Cubes 1, Beacon South Quarter, Sandyford, Dublin 18Dublin 18Apartment2.02.080350000.0C18532-6.21609853.276637175000.000000165438.09523880000.0S8.9407319381423
9539 Mount Argus Green, Harold's Cross, Dublin 6WDublin 6WTerrace3.02.090460000.0C2967-6.28879553.322564153333.33333340264.70588212500.0S3.9610291748156

Last rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
128929843 Kilmartin Gardens, Tallaght, Dublin 24Dublin 24Terrace3.01.092185000.0ZZZ8210-6.39969453.29115261666.666667104899.224806100000.0S11.6553269836629
1290299010 Shamrock Cottages, North Strand, Dublin 3Dublin 3Townhouse2.01.049200000.0G2050-6.24414053.355253100000.000000265227.642276195000.0N0.9499141855134
129129945 Dun Emer Drive, Dundrum, Dublin 14Dublin 14Semi-D4.01.0138645000.0D249-6.23697653.282698161250.000000-71406.250000-95000.0S7.946910107471012
12922998133 Kiltipper Gate, Tallaght, Dublin 24Dublin 24Apartment2.02.070215000.0ZZZ8210-6.37067253.269495107500.00000074899.22480670000.0S11.927989921522
1293300019 Beneavin Park, Glasnevin, Dublin 11Dublin 11Semi-D3.01.097370000.0D2841-6.28328953.389930123333.333333-79768.750000-115000.0N4.4228598954182
129430052 Florence Street, Portobello, Dublin 8Dublin 8Terrace3.02.0124595000.0E28456-6.26972353.331242198333.333333-251540.540541-300000.0S2.5504578353146
129530099 Saint Johns Court, Kilmore Road, Artane, Dublin 5Dublin 5Terrace2.01.071285000.0C31063-6.21893153.390851142500.000000101168.00000090000.0N4.9303231856137
12963026122 Connaught Street, Phibsborough, Dublin 7Dublin 7Terrace3.01.0100550000.0E2841-6.27934553.363448183333.333333-162938.596491-200000.0N1.82382711456114
1297302810 Ard Na Greine, Eaton Brae, off Orwell Road, Rathgar, Dublin 6Dublin 6Terrace2.02.0144900000.0A39460-6.26159553.303673450000.000000-254396.946565-375000.0S5.4977944146178
129830303 Marian Drive, Rathfarnham, Dublin 14Dublin 14Detached5.03.0214850000.0C111062-6.29615753.295243170000.000000-276406.250000-300000.0S6.9102012049119